Application of an LDA Classifier for Determining User-Intent in Multi-DOF Quasi-Static Shoulder Tasks in Individuals with Chronic Stroke: Preliminary Analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Abnormal synergies commonly present after stroke, limiting function and accomplishment of ADL's. They cause co-activation of sets of muscles spanning multiple joints across the affected upper-extremity. These synergies present proportionally to the amount of shoulder effort, thus the effects of the synergy reduce with reduced effort of shoulder muscles. A promising solution may be the application of a wearable exoskeletal robotic device to support the paretic shoulder in hopes to maximize function. To date, control strategies for such a device remain unknown. This work examines the feasibility of using two different linear discriminant analysis classifiers to control shoulder abduction and adduction as well as external and internal rotation simultaneously, two primary degrees of freedom that have gone largely unstudied in hemiparetic stroke. Forces, moments, and muscle activity were recorded during single and dual-tasks involving these degrees of freedom. A classifier that classified all tasks was able to determine user-intent in 14 of the 15 tasks above 90% accuracy. A classifier using force and moment data provided an average 94.3% accuracy, EMG 79%, and data sets combined, 94.9% accuracy. Parallel classifiers identifying user-intent in either abduction and adduction or internal and external rotation were 95.4%, 92.6%, and 97.3% accurate for the respective data sets. These preliminary results indicate that it seems possible to classify user-intent of the paretic shoulder in these degrees of freedom to an adequate accuracy using load cell data or load cell and EMG data combined that would enable control of a powered exoskeletal device.

Original languageEnglish (US)
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2312-2315
Number of pages4
ISBN (Electronic)9781538636466
DOIs
StatePublished - Oct 26 2018
Event40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States
Duration: Jul 18 2018Jul 21 2018

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2018-July
ISSN (Print)1557-170X

Other

Other40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
CountryUnited States
CityHonolulu
Period7/18/187/21/18

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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  • Cite this

    Kopke, J. V., Hargrove, L. J., & Ellis, M. D. (2018). Application of an LDA Classifier for Determining User-Intent in Multi-DOF Quasi-Static Shoulder Tasks in Individuals with Chronic Stroke: Preliminary Analysis. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (pp. 2312-2315). [8512787] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2018.8512787